Maintaining Data Quality Indicators throughout the Life of a Project
Data Quality
Oral Presentation
Prepared by , C. Ballek
Contact Information: chiegel@trihydro.com; 307-745-4993
ABSTRACT
Companies spend thousands of dollars each year to ensure that their data is of sufficient quality to meet the end goals of environmental cleanup or compliance projects. Project managers require that laboratories with the most comprehensive or prestigious certifications, like National Environmental Laboratory Accreditation Conference (NELAC), are used. Laboratory quality practices are ensured through laboratory audits and data validation. In addition, planning documents like sampling and analyses plans and quality assurance project plans are prepared and followed by both sampling personnel and laboratory staff.
Data quality is documented through field notes, laboratory reports, and then the data are qualified through data validation. While the qualifier is important, even more important is the reason behind the qualification. Data qualifiers are important to help indicate what the quality of the data is and how it can be used. For example, understanding a data point qualified “J” for reason of estimation between the reporting limit and method detection limit vs a “J” qualification for reasons related to evidence of field contamination can be significant when assessing compliance. This presentations will cover some lessons learned and best practices for ensuring that not only is data quality assessed but the data quality indicators are maintained throughout the life of the data.
Data Quality
Oral Presentation
Prepared by , C. Ballek
Contact Information: chiegel@trihydro.com; 307-745-4993
ABSTRACT
Companies spend thousands of dollars each year to ensure that their data is of sufficient quality to meet the end goals of environmental cleanup or compliance projects. Project managers require that laboratories with the most comprehensive or prestigious certifications, like National Environmental Laboratory Accreditation Conference (NELAC), are used. Laboratory quality practices are ensured through laboratory audits and data validation. In addition, planning documents like sampling and analyses plans and quality assurance project plans are prepared and followed by both sampling personnel and laboratory staff.
Data quality is documented through field notes, laboratory reports, and then the data are qualified through data validation. While the qualifier is important, even more important is the reason behind the qualification. Data qualifiers are important to help indicate what the quality of the data is and how it can be used. For example, understanding a data point qualified “J” for reason of estimation between the reporting limit and method detection limit vs a “J” qualification for reasons related to evidence of field contamination can be significant when assessing compliance. This presentations will cover some lessons learned and best practices for ensuring that not only is data quality assessed but the data quality indicators are maintained throughout the life of the data.
